Electrical Impedance Tomography for Anisotropic Media: a Machine Learning Approach to Classify Inclusions

TL;DR

This paper introduces a machine learning approach combining neural networks and support vector machines to identify and characterize inclusions and anisotropy in electrical impedance tomography, demonstrating high accuracy with minimal measurements.

Contribution

It presents a novel integration of machine learning techniques with classical EIT analysis to detect inclusions, determine their size, and identify anisotropy from boundary measurements.

Findings

High detection accuracy for inclusions using real and simulated data

Two measurements suffice for accurate size prediction of inclusions

Effective classification of anisotropy within inclusions

Abstract

We consider the problem in Electrical Impedance Tomography (EIT) of identifying one or multiple inclusions in a background-conducting body $\Omega\subset\mathbb{R}^2$, from the knowledge of a finite number of electrostatic measurements taken on its boundary $\partial\Omega$ and modelled by the Dirichlet-to-Neumann (D-N) matrix. Once the presence of one inclusion in $\Omega$ is established, our model, combined with the machine learning techniques of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), may be used to determine the size of the inclusion, the presence of multiple inclusions, and also that of anisotropy within the inclusion(s). Utilising both real and simulated datasets within a 16-electrode setup, we achieve a high rate of inclusion detection and show that two measurements are sufficient to achieve a good level of accuracy when predicting the size of an inclusion. This underscores the substantial potential of integrating machine learning approaches with the more classical analysis of EIT and the inverse inclusion problem to extract critical insights, such as the presence of anisotropy.